At RLI, a Software Engineer plays a pivotal role in designing, building, and maintaining the highly specialized technology platforms that power our niche insurance products. Unlike standard commodity insurance companies, RLI focuses on specialty underwriting, which requires highly customized, flexible, and robust software solutions. As a member of our engineering team, you will directly impact how our underwriters evaluate risk, how claims are processed, and how our customers interact with our digital products. This position is highly collaborative, bridging the gap between complex insurance operations and modern software architectures. You will work on a variety of mission-critical applications, ranging from transactional web systems to modern cloud-native solutions. Your work will ensure that RLI remains agile, secure, and capable of delivering excellent service to our clients at scale. Whether you are joining as a Senior Software Engineer or a Cloud Solutions Architect, your contributions will help drive the modernization of our technology stack. You will have the opportunity to work with modern technologies like,, and, helping us transition legacy systems into scalable, cloud-first platforms. It is an exciting, high-ownership environment where your technical decisions have a direct line of sight to business outcomes..NET C# Azure
Recruiter Call
reportedA conversational video call with a recruiter to discuss your background and the role.
What to demonstrate
- A conversational video call with a recruiter to discuss your background and the role
- Depth in Software Engineering
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Discussion
reportedDeeper technical discussions focusing on your past achievements and software engineering philosophy.
What to demonstrate
- Deeper technical discussions focusing on your past achievements and software engineering philosophy
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: Tell me about a time when a technical decision you made during a project did not go as planned. How did you pivot?
- Answer aloud and timed: How do you approach database design and optimization when working on high-throughput applications?
Behavioral Discussion
reportedConversations about how you approach system design and work in a collaborative environment.
What to demonstrate
- Conversations about how you approach system design and work in a collaborative environment
- Depth in Software Engineering
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral discussion above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Lead with your project stories
Since our interviews heavily focus on your past work, spend time before the interview mapping out 2-3 significant projects. Be ready to explain the business problem, the technical architecture, your specific contributions, and the ultimate outcome.
Emphasize the "Why", not just the "What
When describing a technology choice or architectural design, always explain the reasoning behind your decision. Showing that you weigh trade-offs is a key indicator of seniority.
Showcase your collaborative nature
We value team players who can work across departments. Use "we" when discussing team successes, but be clear about your individual responsibilities ("I designed the database schema, while my teammate built the frontend").
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you implement asynchronous programming in C#, and what are some common pitfalls to avoid?
How do you implement asynchronous programming in C#, and what are some common pitfalls to avoid?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Describe a situation where you had to work under tight deadlines with ambiguous requirements. How did you prio
Describe a situation where you had to work under tight deadlines with ambiguous requirements. How did you prioritize your tasks?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
Explain why the owner filter ignores the listing index
The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.
Approach
- Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
- For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
- Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
- Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.
Follow-up
- 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
- A dashboard runs this for 40 owners in one page load. What changes about the design?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
Walk me through the architecture of the most complex software system you have designed or maintained in your p
Walk me through the architecture of the most complex software system you have designed or maintained in your previous roles.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Describe a project where you had to refactor a legacy application. What challenges did you face, and how did y
Describe a project where you had to refactor a legacy application. What challenges did you face, and how did you manage the transition?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you approach database design and optimization when working on high-throughput applications?
How do you approach database design and optimization when working on high-throughput applications?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Explain the differences between dependency injection lifetimes in.NET Core and how you choose which one to use
Explain the differences between dependency injection lifetimes in.NET Core and how you choose which one to use.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Describe your approach to writing clean, testable code. How do you balance speed of delivery with architectura
Describe your approach to writing clean, testable code. How do you balance speed of delivery with architectural purity?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
What design patterns do you find yourself using most frequently, and in what scenarios do they apply?
What design patterns do you find yourself using most frequently, and in what scenarios do they apply?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How do you approach migrating an on-premises.NET application to Azure? What factors influence your choice of s
How do you approach migrating an on-premises.NET application to Azure? What factors influence your choice of services?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How do you design for high availability and disaster recovery in a cloud-first environment?
How do you design for high availability and disaster recovery in a cloud-first environment?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Read latency spikes on a sixty-second sawtooth
The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.
Approach
- Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
- Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
- Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
- De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.
Follow-up
- The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
- How does your answer change if the recompute takes 4 seconds instead of 250 ms?
Built from the rounds and topics RLI candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the RLI loop
- Write out the reported sequence: Recruiter Call, Technical Discussion, Behavioral Discussion.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.
02Work Software Engineering
- Spend the session on Software Engineering, which RLI candidates report being tested on.
- Write one worked example in Software Engineering and time yourself on it.
Deliverable: One timed worked example in Software Engineering.
03Work Cloud Solutions Architecture
- Spend the session on Cloud Solutions Architecture, which RLI candidates report being tested on.
- Write one worked example in Cloud Solutions Architecture and time yourself on it.
Deliverable: One timed worked example in Cloud Solutions Architecture.
04Work .NET
- Spend the session on .NET, which RLI candidates report being tested on.
- Write one worked example in .NET and time yourself on it.
Deliverable: One timed worked example in .NET.
05Answer out loud: Project Walkthroughs & Past Experience
- Answer aloud, timed: Walk me through the architecture of the most complex software system you have designed or maintained in your previous roles.
- Answer aloud, timed: Describe a project where you had to refactor a legacy application. What challenges did you face, and how did you manage the transition?
Deliverable: Spoken answers to 2 reported Project Walkthroughs & Past Experience question(s), under time.
06Answer out loud: .NET & Application Architecture
- Answer aloud, timed: Explain the differences between dependency injection lifetimes in.NET Core and how you choose which one to use.
- Answer aloud, timed: How do you implement asynchronous programming in C#, and what are some common pitfalls to avoid?
Deliverable: Spoken answers to 2 reported .NET & Application Architecture question(s), under time.
07Answer out loud: Cloud & Infrastructure Foundations
- Answer aloud, timed: How do you approach migrating an on-premises.NET application to Azure? What factors influence your choice of services?
- Answer aloud, timed: Describe your experience with infrastructure as code (IaC) and continuous integration/continuous deployment (CI/CD) pipelines.
Deliverable: Spoken answers to 2 reported Cloud & Infrastructure Foundations question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Tell me about a time when a technical decision you made during a project did not go as planned. How did you pi
Tell me about a time when a technical decision you made during a project did not go as planned. How did you pivot?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe your experience with infrastructure as code (IaC) and continuous integration/continuous deployment (C
Describe your experience with infrastructure as code (IaC) and continuous integration/continuous deployment (CI/CD) pipelines.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Tell me about a time when you had to explain a complex technical concept to a non-technical stakeholder or bus
Tell me about a time when you had to explain a complex technical concept to a non-technical stakeholder or business partner.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you handle disagreements within your engineering team regarding technical direction or architecture?
How do you handle disagreements within your engineering team regarding technical direction or architecture?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
Tell me about a time when a technical decision you made during a project did not go as planned. How did you pivot?
- 02
Describe your experience with infrastructure as code (IaC) and continuous integration/continuous deployment (CI/CD) pipelines.
- 03
Tell me about a time when you had to explain a complex technical concept to a non-technical stakeholder or business partner.
- 04
How do you handle disagreements within your engineering team regarding technical direction or architecture?
How technical are the face-to-face interview rounds?
The technical rounds are highly conversational and focused on your real-world experience. Rather than asking you to solve abstract algorithmic puzzles on a whiteboard, our engineers will ask you to explain the architecture of systems you have built, discuss design patterns, and talk through how you solve practical software development problems.
RLI Software Engineer candidate reports ↗What is the company culture like for engineers at RLI?
RLI has a highly collaborative, supportive, and employee-centric culture. Teams are close-knit, and there is a strong emphasis on work-life balance, mutual respect, and professional growth. Interviewers are genuinely friendly and want to see you succeed.
RLI Software Engineer candidate reports ↗Where are the software engineering teams located?
Our primary technology hubs are in Peoria, IL, Alpharetta, GA, and Tempe, AZ. We support collaborative working models, but physical proximity to one of our key offices is often preferred for team alignment and collaboration.
RLI Software Engineer candidate reports ↗How long does the interview process typically take?
The process is efficient and typically takes between two to four weeks from the initial recruiter screen to the final decision, depending on scheduling availability.
RLI Software Engineer candidate reports ↗What topics does RLI test in interviews?
RLI interviews most often cover Insurance Domain Knowledge, Business Analysis, Financial Analysis, Technical Underwriting, and Software Engineering. The exact emphasis depends on the specific role you apply for.
RLI Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01RLI Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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